DNN are prone to reproducing or emphasizing biases that are contained in the data used to train them.
Different approaches to detect and mitigate bias have been introduced.
For example, [1]} shows racial discrimination in the online ad delivery by Google and [2]} debias commonly used word embeddings.
More recently, researchers investigate biases in modern transformer-based models like BERT [3]}, for example focusing on gender bias [4]} or ethnic bias [5]}.
Discrimination in facial recognition algorithms is evaluated by [6]} and they introduced the Gender Shades data set with a balanced distribution of gender and skin type.
Similarly, [7]} introduced the Fair Face data set which is balanced for gender, race and age categories.
In this work, we perform racial bias detection as an exemplary use case of our novel visualization technique.
Different to [6]} or [7]}, who investigate bias in the output and the performance of algorithmic decision systems, we focus on bias in the representations of the individual layers of a DNN.
